A Distributed Clustering Algorithm for
Target Tracking in Vehicular Ad
-
Hoc
Networks
Dr.
Khalil
El
-
Khatib
, Dr. Richard
Pazzi
,
Sanaz
Khakpour
Table of Contents
-
Introduction
-
Related Work
-
Algorithm Features
-
Algorithm Overview
-
Algorithm Description
-
Conclusion
Introduction
•
VANETs
are
network
of
autonomous
mobile
nodes
that
communicate
with
each
other
without
any
fixed
infrastructure
.
•
VANETs
are
large
-
scale
networks
and
dividing
the
network
into
smaller
clusters
in
such
dynamic
environment
is
an
advantageous
technic
.
Related Work
•
MANET and WSN clustering algorithms do not work
properly in VANET environment.
•
The most important challenge in clustering algorithms
that most of the protocols are trying to solve are:
Optimal cluster management in VANET’s highly
dynamic environment.
Increasing cluster stability (MDMAC, SBCA)
Prevention of frequent cluster changes
Increasing cluster head availability (SBCA)
Increasing cluster lifetime by using appropriate
mobility metrics (DCA, MDCAM, DMAC, SBCA,
MOBIC, …)
Special features of the proposed algorithm
•
A cluster
-
based target tracking algorithm
•
high
cluster head and cluster lifetime
•
robust and stable clusters
•
low
delay and overhead for electing new cluster
head in lost CH scenarios
•
distributed cluster head selection
mechanism
Table of Contents
-
Introduction
-
Related Work
-
Algorithm Features
-
Algorithm Overview
-
Algorithm Description
-
Conclusion
Assumptions and Definitions
•
The
proposed
clustering
algorithm
was
designed
for
vehicle
tracking
in
VANETs
.
•
This
algorithm
assumes
that
vehicles
have
front
and
rear
cameras
and
can
detect
visual
features
of
a
target
.
•
A
central
entity
such
as
a
police
station
is
seeking
help
to
find
a
specific
target
.
This
entity
is
called
Control
Centre
(CC)
and
is
a
node
located
in
multi
-
hop
communication
distance
from
target
.
Tracking Failure Probability Metric (TFP)
Assumptions
:
•
All
vehicles
are
aware
of
their
location
and
velocity
by
using
a
GPS
device
.
•
The
location
of
a
target
is
unknown
;
But
can
be
calculated
by
visual
processing
.
•
To
calculate
TFP
between
a
vehicle
A
and
the
target
vehicle
T
at
time
t,
we
need
to
have
the
distance
between
node
A
and
T
and
their
speed
vector
at
that
time
.
•
We
define
a
value
called
Valid
Distance
Range
(VDR),
which
is
used
to
normalize
the
distance
between
any
node
and
target
.
Tracking Failure Probability Metric (TFP)
•
The
normalized
distance
is
calculated
as
follow
:
(
1
)
𝐷
𝐴
𝑁𝑡
=
𝐷
𝐴𝑇
𝑡
𝑉𝐷𝑅
•
By
using
velocity
vectors
of
vehicles
we
can
differentiate
between
nodes
moving
in
the
same
direction
and
nodes
moving
in
opposite
direction
.
𝑉
𝐴
𝑡
is
defined
as
:
(
2
)
𝑉
𝐴
𝑡
=
𝑉
𝐴
𝑡
𝑐𝑜
𝜃
•
We
use
a
value
called
Valid
Velocity
Range
(VVR)
in
order
to
normalize
the
value
of
velocity
vectors
.
Tracking Failure Probability Metric (TFP)
•
V
and
V
Are
normalized
velocity
vectors
of
vehicle
A
and
target
T
respectively
.
(
3
)
𝑉
𝐴
𝑁𝑡
=
𝑉
𝐴
𝑡
𝑉𝑉𝑅
(
4
)
𝑉
𝑇
𝑁𝑡
=
𝑉
𝑇
𝑡
𝑉𝑉𝑅
•
Two
values
α
and
β
are
defined
as
Distance
and
speed
Efficiency
Factors
.
These
values
are
coefficients
of
distance
and
velocity
to
control
efficiency
of
these
metrics
of
each
vehicle
.
•
The
TFP
of
node
A
at
time
t
is
calculated
as
in
the
following
formula
.
The
lower
TFP
indicates
higher
priority
to
become
cluster
head
.
(
5
)
𝑇𝐹𝑃
(
𝐴
)
𝑡
=
100
*
(
𝛼
𝐷
𝐴
𝑁𝑡
+
β
𝑉
𝐴
𝑁𝑡
−
𝑉
𝑇
𝑁𝑡
)
Table of Contents
-
Introduction
-
Related Work
-
Algorithm Features
-
Algorithm Overview
-
Algorithm Description
-
Conclusion
Table of Contents
-
Introduction
-
Related Work
-
Algorithm Features
-
Algorithm Overview
-
Algorithm Description
-
Control Centre Functions
-
Initialization Phase
-
Cluster Management Phase:
o
Cluster Head Functions
o
Cluster Members Functions
-
Tracking Phase
-
Conclusion
Control Centre (CC)
•
CC broadcasts a “Target Tracking Request Message”
(TTRM) to the entire network with target vehicle’s visual
information.
•
When CC receives “Response Message” from any
vehicle that has located the target, it will stop
broadcasting.
•
At any point later, if the CC stops receiving any
information from the cluster head regarding the
specified target (after a pre
-
defined time interval) it will
assume the cluster no longer exists and it will start
broadcasting target’s information again in the network.
Initialization Phase
•
Any
vehicle
that
receives
a
TTRM
message
from
the
Control
Center
(CC)
and
which
can
detect
the
target
responds
to
CC
and
starts
the
initialization
process
.
•
OBNs start broadcasting “Request Messages” to their
neighbors
and receive “Response Messages” from
them. OBNs also check the TDV field of the response
messages.
•
OBNs calculate their TFP. This value displays which
vehicle has closer movement pattern to target and is
more appropriate to be the cluster head.
Initialization Phase
•
Cluster
members
are
divided
into
2
groups
:
level
-
1
(OBNs)
and
Level
-
2
(NNs)
.
•
Member
nodes
are
connected
to
cluster
instead
of
cluster
head
.
•
Initialization
phase
might
be
repeated
only
if
there
is
not
any
cluster
members
available
and
the
cluster
is
destroyed
.
•
The
purpose
of
our
design
is
to
avoid
switching
to
Initialization
Phase
from
Cluster
Maintenance
phase
.
•
After
this
phase
the
initial
cluster
is
created
and
the
cluster
head
is
selected
.
Cluster Maintenance Phase
(Cluster Head Functions)
•
CH
is
responsible
of
managing
the
cluster
by
sending
request
messages
at
every
time
intervals
to
find
new
cluster
members
.
•
the
cluster
head
calculates
its
own
TFP
every,
and
compares
it
with
other
values
in
the
neighbour
list
to
check
if
it
is
still
a
valid
CH
.
If
not
it
will
send
a
“Resign
Message”
.
•
A
“Safe
Threshold”
is
defined
because
the
TFPs
are
changing
so
quickly
and
we
do
not
want
to
change
CH
so
frequently
.
•
vehicles
moving
in
opposite
direction
of
the
target
are
not
supposed
to
join
cluster
because
these
nodes
are
unstable
cluster
members
and
will
decrease
cluster’s
stability
.
Cluster Maintenance Phase
(Cluster Members Functions)
•
OBNs
calculate
their
TFP
every
𝑝
time
interval
and
send
it
in
RPM
to
their
neighbors
.
Also,
OBNs
store
the
TFP
of
other
nodes
in
their
neighbor
list
.
•
If
member
nodes
receive
a
RSM
they
are
responsible
to
find
a
node
with
lowest
TFP
value
in
their
neighbor
list
and
select
it
as
CH
.
•
Also,
if
a
member
node
does
not
receive
any
kind
of
message
after
a
defined
time
interval,
it
assumes
to
be
out
of
cluster
borders
and
will
try
to
send
its
information
directly
to
CC
(if
possible)
.
Tracking Phase
•
Tracking
includes
taking
continuous
video
of
target
and
sending
video
data
and
location
information
of
target
to
CC
during
specified
time
intervals
.
•
CMs
send
their
data
to
CH
and
CH
is
responsible
to
inform
CC
about
target’s
location
.
•
In
case
CM
goes
out
of
cluster
boundaries
(and
does
not
have
access
to
CH),
it
should
send
the
latest
information
to
CC
.
Table of Contents
-
Introduction
-
Related Work
-
Algorithm Features
-
Algorithm Overview
-
Algorithm Description
-
Conclusion
Conclusion
•
Introduced
algorithm
aims
to
improve
cluster
performance
by
making
stable
and
long
living
cluster
.
•
The
stability
of
this
algorithm
is
mostly
because
of
adding
candidate
cluster
members
which
are
highly
probable
of
detecting
target
in
near
future
.
•
The
TFP
value
is
used
as
an
evaluation
value
to
compare
movement
pattern
of
vehicles
with
target
.
•
The
idea
of
distributed
cluster
head
selection
is
introduced
by
use
of
TFP
.
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